DRL-STNet: Unsupervised Domain Adaptation for Cross-Modality Medical Image Segmentation via Disentangled Representation Learning

Hui Lin; Florian Schiffers; Santiago López-Tapia; Neda Tavakoli; Daniel Kim; Aggelos K. Katsaggelos

doi:10.1007/978-3-031-96202-8_15

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DRL-STNet: Unsupervised Domain Adaptation for Cross-Modality Medical Image Segmentation via Disentangled Representation Learning

Hui Lin Florian Schiffers Santiago López-Tapia Neda Tavakoli Daniel Kim Aggelos K. Katsaggelos

Year: 2025 Lecture notes in computer science Pages: 178-194 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-031-96202-8_15

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Keywords:

Computer science Domain adaptation Representation (politics) Modality (human–computer interaction) Artificial intelligence Adaptation (eye) Domain (mathematical analysis) Segmentation Image segmentation Image (mathematics) Feature learning Computer vision Pattern recognition (psychology) Mathematics Psychology

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AI in cancer detection

Physical Sciences → Computer Science → Artificial Intelligence

Medical Imaging and Analysis

Physical Sciences → Engineering → Biomedical Engineering

COVID-19 diagnosis using AI

Health Sciences → Medicine → Radiology, Nuclear Medicine and Imaging

DRL-STNet: Unsupervised Domain Adaptation for Cross-Modality Medical Image Segmentation via Disentangled Representation Learning

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